Reconfigurable terahertz metamaterials: From fundamental principles to advanced 6G applications

Xu, Cheng; Ren, Zhihao; Wei, Jingxuan; Lee, Chengkuo

doi:10.1016/j.isci.2022.103799

Cited by 64 publications

(34 citation statements)

References 203 publications

(414 reference statements)

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“…, from Gbps to Tbps level), giving the abundant bandwidth, low latency, and improved data transfer rate ( Elayan et al., 2019 ; Rappaport et al., 2019 ). Furthermore, THz sensors have attracted much attention because of their non-destructive properties as well as being able to observe the intramolecular and intermolecular vibrational modes of many chemical and biological macromolecules in this region ( Xu et al., 2022 ). However, due to the limitations of electronic and optical devices in this region, known as the THz gap, it was difficult to efficiently switch between electromagnetic waves and electrical power, and there is a lack of related functional devices to fill this technology gap.…”

Section: Tunable Photonics For Terahertz Plasmonic Modulationmentioning

confidence: 99%

“…The PCMs’ large variations in optical properties and great compatibility with complementary metal-oxide semiconductor (CMOS) ( e.g. Ge-Sb-Te (GST), VO 2 ) facilitate the realization of highly compact switchable photonic applications including metasurface, neuromorphic photonic memory device, and terahertz photonic devices, over the broad spectral range ( Abdollahramezani et al., 2020 ; Che et al., 2020 ; Jeong et al., 2020 ; Neubrech et al., 2020 ; Shastri et al., 2021 ; Xu et al., 2022 ). Their optical modulation stems from the changes in their atomic bonding configuration according to metal-insulator transition (MIT).…”

Section: Introductionmentioning

confidence: 99%

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A review of tunable photonics: Optically active materials and applications from visible to terahertz

Yoo

Lee

et al. 2022

iScience

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Section: Tunable Photonics For Terahertz Plasmonic Modulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A review of tunable photonics: Optically active materials and applications from visible to terahertz

Yoo

Lee

et al. 2022

iScience

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“…Such relationships allow for very efficient meta-analyses, potentially uncovering new physical relationships through data-driven methods (e.g., symbolic regression [60] ), or facilitating the design of more complex metasurfaces with novel functionalities. [61,62] 2200097 (9 of 10) www.advopticalmat.de Adv. Optical Mater.…”

Section: Comparison To Conventional Dnnsmentioning

confidence: 99%

Learning the Physics of All‐Dielectric Metamaterials with Deep Lorentz Neural Networks

Khatib

Ren

Malof

et al. 2022

Advanced Optical Materials

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spectral response. However, ADM resonators are only moderately sub-wavelength, with modes containing evanescent tails that persist outside the structure surface, giving significant neighboring interactions that make direct functional relationships between unit-cell geometry and complex scattering spectra difficult or impossible to obtain. Consequently, conventional optimization approaches for designing super-unit-cells to realize desired or novel scattering properties become infeasible or impractical, requiring new approaches.Deep learning has emerged as an exceptionally efficient tool for the design and modeling of metamaterials. [27][28][29][30][31][32][33] Using a data-driven approach, feedforward deep neural networks (DNNs) can be trained to approximate the functional map from geometry to scattering spectra highly accurately. A well-trained DNN can thus act as a surrogate model for the underlying physics of ADMs, including those with complex super-unit-cells. [34,35] As a result, neural surrogate models can often effectively replace commercial electromagnetic simulation software, allowing for orders of magnitude faster calculation of scattering properties for a given metamaterial geometry. [35] Additionally, DNNs can facilitate the inverse design of metamaterials, where a specific target scattering response is used to guide the optimization of unit cell geometrical parameters, in order to achieve the desired electromagnetic behavior.Despite the tremendous recent progress in applications of deep learning to the forward and inverse design of artificial electromagnetic materials, DNNs are "black box" models that efficiently interpolate between training samples, and generally only approximate the true underlying physical relationships. As a consequence, neural surrogate models, which require significant quantities of training data, [27] suffer from poor generalization outside the training domain. [35] For the same reasons, neural surrogate models are generally not interpretable/explainable, and provide little insight into the underlying physics that they approximate.Recent research has shown that some of the limitations of conventional DNNs can be mitigated by employing fundamental physical knowledge to guide or constrain the DNN training process, or the network architecture, in different ways. These types of models have a variety of names and implementation, but are typically described as "physics-informed" or "physics-guided" neural networks. One prominent example, using partial differential equations to regularize training, [36] has Deep neural networks (DNNs) have shown marked achievements across numerous research and commercial settings. Part of their success is due to their ability to "learn" internal representations of the input (x) that are ideal to attain an accurate approximation ( f f ) of some unknown function (f) that is, y = f(x). Despite their universal approximation capability, a drawback of DNNs is that they are black boxes, and it is unknown how or why they work. Thus, the physics discovered by ...

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“…Metamaterial is an artificial composite material that possesses unique electromagnetic properties [ 1 , 2 ]. The purpose of metamaterial is to break through the limitation of conventional nature materials.…”

Section: Introductionmentioning

confidence: 99%

Tunable MEMS-Based Terahertz Metamaterial for Pressure Sensing Application

Lai

et al. 2023

Micromachines

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In this study, a tunable terahertz (THz) metamaterial using the micro-electro-mechanical system (MEMS) technique is proposed to demonstrate pressure sensing application. This MEMS-based tunable metamaterial (MTM) structure is composed of gold (Au) split-ring resonators (SRRs) on patterned silicon (Si) substrate with through Si via (TSV). SRR is designed as a cantilever on the TSV structure. When the airflow passes through the TSV from bottom to up and then bends the SRR cantilever, the SRR cantilever will bend upward. The electromagnetic responses of MTM show the tunability and polarization-dependent characteristics by bending the SRR cantilever. The resonances can both be blue-shifted from 0.721 THz to 0.796 THz with a tuning range of 0.075 THz in transverse magnetic (TM) mode and from 0.805 THz to 0.945 THz with a tuning range of 0.140 THz in transverse electric (TE) mode by changing the angle of SRR cantilever from 10° to 45°. These results provide the potential applications and possibilities of MTM design for use in pressure and flow rate sensors.

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Reconfigurable terahertz metamaterials: From fundamental principles to advanced 6G applications

Cited by 64 publications

References 203 publications

A review of tunable photonics: Optically active materials and applications from visible to terahertz

A review of tunable photonics: Optically active materials and applications from visible to terahertz

Learning the Physics of All‐Dielectric Metamaterials with Deep Lorentz Neural Networks

Tunable MEMS-Based Terahertz Metamaterial for Pressure Sensing Application

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